Real-time analytics databases have been absolute game changers for businesses everywhere. They are designed to ingest, process, and analyze vast amounts of data in real-time, providing organizations with brand new information for rapid decision-making.
As companies across industries strive to gain a competitive edge through data-driven strategies, understanding a real time analytics database is essential for growth in an increasingly dynamic and competitive market.
What is a real time analytics database?
A real-time analytics database is a specialized data management system engineered for real-time analytics. In other words, these databases can gather and analyze large volumes of data as it is generated or received, typically within milliseconds to seconds.
Real time analytics can be a new concept for many businesses
To handle these tasks, they are optimized for high-speed data ingestion, low-latency querying, and continuous analysis of streaming data. Unlike traditional databases that focus on storing and retrieving historical data, real-time analytics databases have the ability to handle the velocity, volume, and variety of data in motion.
This capability enables organizations to make the best possible decisions based on the most up-to-date and relevant information available, driving agility and responsiveness in various business operations.
How to choose a good real time analytics database
What makes a database perfect for real-time analytics? Looking into these functions will help you understand and single out real time analytics databases from traditional ones:
Efficient data ingestion
Real time analytics databases should be able to rapidly absorb large volumes of data from multiple sources simultaneously, that means handling millions of data points per second and ensuring that information is available for analysis almost instantaneously.
To achieve this, these databases employ advanced techniques such as stream processing for continuous data ingestion and analysis, and micro-batching, which divides the input into chunks of data at very short intervals.
Data ingestion is a major mechanic of real time databases
Efficiency in ingesting and analyzing data is significant as it enables organizations to capture and analyze data from diverse sources and ensures that businesses can maintain high data freshness of their operations, market conditions, or customer behavior, facilitating timely and informed decision-making.
Read patterns
These features in real-time analytics databases can support rapid data retrieval and analysis by using advanced query optimization techniques like filtering and aggregating to deliver effective results.
Think of it as the ability to quickly find a specific collection of books or identify certain patterns in a vast, constantly updating library. Real-time analytics databases can pinpoint and analyze relevant data in milliseconds.
Columnar storage formats reign supreme when it comes to read patterns since they organize data by columns rather than rows. This structure allows for efficient compression and faster query execution because data is stored sequentially, therefore reducing scan size.
Performance
Query performance refers to how fast the database can handle requests. And since most real time analytics databases are synonymous with user-facing analytics, performance is extremely important for the overall user experience.
While there is no particular range or number, if the database takes longer than 50 to 100 milliseconds to process data, it can significantly tank the user experience.
The performance of a database is also important
Real time analytics databases should be able to handle requests promptly without extra configuration or usage of optimization mechanisms. Aside from that, columnar databases can also enhance performance, since they can read the most essential data, subsequently saving precious processing time.
Concurrency
Also related to user experiences is concurrency, which is the system’s ability to handle multiple simultaneous operations without compromising performance. This means the database can process numerous queries and data updates at the same time, ensuring that multiple teams, users, or applications can access and analyze data without any hindrance.
It does not matter if the queries are complex, real time databases should be able to process them effectively. Even if you do not expect a large number of users, you should always be prepared in terms of concurrency, since it can be quite costly to upgrade.
Scalability
Upgrading is unavoidable and you should always consider a database’s scalability to adapt seamlessly to growing data volumes and user demands in the future. These systems are designed to scale both vertically, by adding more resources to a single node, and horizontally, by distributing data and processing across multiple nodes.
It is important to note that scaling should maintain the database’s abilities like high data freshness, low latency, and high query performance.
Ease of use
Lastly, you should always prioritize choosing a database that works best for you. Real time analytics databases can only work their magic if they can fit into your day-to-day operations seamlessly. And to ensure this, you should choose ones that are easy to deploy.
Another tip would be to avoid highly specialized databases unless you have a firm understanding of how they work. Lastly, do not base your decision solely on the performance of the database, since it may not be the best match for you.
The best database for you should be easy to use
What are some major real time analytics databases?
To help your business make a good decision when choosing your real time analytics database, here are three prominent options currently available:
ClickHouse
ClickHouse is a high-performance, open-source, and column-oriented database designed specifically for real-time analytics. Thanks to its excellent query performance, ClickHouse can handle large volumes of data with exceptional speed and efficiency, making it the ideal choice for businesses working with copious amounts of data.
Additionally, its architecture also allows for rapid data ingestion and low-latency queries while also supporting parallel and distributed processing. Lastly, with significant SQL support, ClickHouse presents itself as a powerful solution for complex joins and subqueries.
There are quite a few real time databases to choose from
Apache Druid
Apache Druid is a high-performance real-time analytics database designed for fast aggregation and exploration of large datasets. Same as ClickHouse, Druid is ideal for time-series data with low-latency capabilities.
Its flexible ingestion model is what sets it apart from other databases, catering to a wide variety of sources. Aside from that, horizontal scalability and support for approximate queries make Druid a more than capable database for real-time analytics. Lastly, its column-oriented storage has made it a very popular choice for organizations seeking to build large-scale, real-time data analytics platforms.
Apache Pinot
The third suggestion is Apache Pinot, a columnar, open-source database made by LinkedIn for fast aggregation and exploration of large datasets. Known for its ability to ingest massive volumes of event data and provide low-latency queries, Pinot proves to be a highly capable option in real time analytics.
On top of that, Pinot also supports various streaming data sources alongside various advanced features related to indexing and scalability. However, Pinot is not without some minor weaknesses due to its usage of MySQL dialect, which can be somewhat lacking in the modern language department for real-time analytics.
The importance of real-time analytics databases
The ability to derive instant insights from data can translate to a significant competitive advantage in all industries, which makes real-time analytics an essential for businesses looking for a major breakthrough.
As brands strive to respond swiftly to market changes and customer needs, these databases play a significant role in enabling data-driven and timely decision-making.
How do they do it? These databases are primarily a backbone for modernizing data infrastructures. They enable businesses to move beyond traditional analysis methods, facilitating a shift towards a more flexible approach to keep up with the ever-evolving market and consumer behaviors.
Real time analytics can be a game changer for many businesses
And once the decision-making processes can occur seamlessly on the foundation of fresh and relevant data, organizations can craft their business development strategies accordingly, putting them in an advantageous position for a major boost in customer service quality, sales, revenue, and profit.
This capability is particularly vital in industries such as e-commerce, where real-time product recommendations can significantly impact sales, or in financial services, where instant fraud detection is critical.
It is important to establish trust and win over consumers before they move on to other options, and incorporating real-time analytics databases into your operation can help you do just that.
Pros and cons of using a real time analytics database
There are two sides to every coin, and real time analytics is no exception. With great advantages on a market come steep hills to climb such as understanding the concept of these databases and training your workforce:
Advantages
Real-time analytics databases offer several significant advantages that can transform how businesses operate and compete in today’s fast-paced market. Aside from allowing brands to make swift and appropriate decisions based on the most current data available, they also offer tons of other perks.
Data visualization is one of the major advantages. It presents complex information through intuitive dashboards and interactive reports, turning them into highly comprehensible insights, which enhances understanding and accelerates decision-making processes.
Another advantage that may fly under the radar is the relatively lower long-term costs compared to other analytics methods.
While they may require more investment to deploy, these databases enable efficient operations, reduce reaction times, and minimize the need for extensive historical data storage. Over time, they can contribute to significant cost savings across various business functions.
Disadvantages
On the other hand, the term “real-time analytics” can be ambiguous, leading to misaligned expectations. “Real-time” can vary significantly across industries and use cases, ranging from milliseconds to minutes.
Additionally, a real-time analytics database demands an overhaul of existing data infrastructure. Not to mention the cost, the effort to identify and deploy high-performance hardware, robust network capacity, and specialized software components can be really complicated for a business without a specialized team.
After implementing the new infrastructure, businesses must also ensure that their employees have a firm understanding of real-time analytics databases to manage these systems, optimize queries, and interpret real-time data. It will take a considerable amount of time to bridge the skill gaps in the workforce as they adapt to the change.
And lastly, there’s a prevalent misconception that real-time analytics is a goal instead of a tool. Some businesses consider it a necessity to the point where they would make adjustments to their internal processes just to accommodate a real-time analytics database. The best course of action is to choose a database that best fits your business objectives.
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A real time analytics database comes with a lot of benefits for businesses in the modern era, from facilitating their business operation to enabling them to learn about new developments promptly.
However, this concept is still somewhat foreign for many businesses and they can encounter some difficulties implementing these databases. Make sure to brush up your knowledge of data analytics and choose your preferred databases before committing to using real-time analytics.